Machine and equipment assets, generally, are engineered to perform particular tasks as part of a business process. For example, assets can include, among other things and without limitation, industrial manufacturing equipment on a production line, drilling equipment for use in mining operations, wind turbines that generate electricity on a wind farm, transportation vehicles such as trains and aircraft, and the like. As another example, assets may include devices that aid in diagnosing patients such as imaging devices (e.g., X-ray or MRI systems), monitoring equipment, and the like. The design and implementation of these assets often takes into account both the physics of the task at hand, as well as the environment in which such assets are configured to operate.
Low-level software and hardware-based controllers have long been used to drive machine and equipment assets. However, the rise of inexpensive cloud computing, increasing sensor capabilities, and decreasing sensor costs, as well as the proliferation of mobile technologies have created opportunities for creating novel industrial and healthcare based assets with improved sensing technology and which are capable of transmitting data that can then be distributed throughout a network. As a consequence, there are new opportunities to enhance the business value of some assets through the use of novel industrial-focused hardware and software.
The machines and equipment that are installed in a manufacturing environment are often quite big in size and/or complex in their operations. Issues in these machines can also be quite complex and require the service of highly skilled engineers, also referred to herein as subject matter experts. When the subject matter expert performing a work order is new to the job or is not highly skilled, it can take hours, or even longer, for the expert to resolve the issues. For example, the subject matter expert typically analyzes dozens if not hundreds of sources of information, evaluates the information, and makes a best guess as to the issues and reasons for error/failure associated with the particular asset. In order to make such a determination, a subject matter expert often analyzes textual based data such as repair orders, work orders, service orders, notes made by engineers/technicians in the field, materials used, and the like. After analyzing all of this data, a subject matter expert then makes the best-guess as to the cause of an asset failure.